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结直肠癌中血管生成相关亚型的串扰、预后特征模型的建立及免疫浸润特征分析。

Crosstalk of angiogenesis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in colorectal adenocarcinoma.

机构信息

Department of Gastroenterology, The Second Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China.

Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.

出版信息

Front Immunol. 2022 Nov 24;13:1049485. doi: 10.3389/fimmu.2022.1049485. eCollection 2022.

DOI:10.3389/fimmu.2022.1049485
PMID:36505481
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9731117/
Abstract

BACKGROUND

Colorectal adenocarcinoma (COAD) is one of the most common malignancies and angiogenesis is vital to the development of cancer. Here, we explored the roles of angiogenesis-related genes (ARGs) that affect the prognosis of COAD and constructed risk models to assess patient prognosis, immune characteristics, and treatment outcomes.

METHODS

We comprehensively characterized the transcriptional and genetic modifications of 48 ARGs in COAD and evaluated the expression patterns. We identified two ARG subgroups using the consensus clustering algorithm. Based on the differentially expressed genes (DEGs) of two ARG subtypes, we calculated risk score, namely ARG_scores, and calssified COAD patients into different risk groups. To investigate the expression of ARG_score-related genes, qRT-PCR was performed. Subsequently, we mapped the nomogram to visually and accurately describe the value of the application of ARG_score. Finally, the correlation between ARG_score and clinical features, immune infiltration along with drug sensitivity were explored.

RESULTS

We identified two ARG related subgroups and there were great differences in overall survival (OS) and tumor microenvironment. Then, we created an ARG_score for predicting overall survival based on eight DEGs and confirmed its reliable predictive power in COAD patients, with higher ARG_score associated with worse prognosis. Furthermore, eight ARG_score-related genes expression was investigated by qRT-PCR. To make the ARG_score clinically feasible, we created a highly reliable nomogram. We also found a higher proportion of microsatellite instability-high (MSI-H) and higher tumor mutational burden (TMB) in the high-risk group. In addition, ARG_score was notably correlated with cancer stem cell indices and drug sensitivity.

CONCLUSION

This scoring model has potential clinical application value in the prognosis, immune microenvironment and therapeutic drug sensitivity of COAD, which provides new insights for personalized treatment.

摘要

背景

结直肠腺癌(COAD)是最常见的恶性肿瘤之一,血管生成对癌症的发展至关重要。在这里,我们探讨了影响 COAD 预后的血管生成相关基因(ARGs)的作用,并构建了风险模型来评估患者的预后、免疫特征和治疗结果。

方法

我们全面描述了 48 个 ARG 在 COAD 中的转录和遗传修饰,并评估了它们的表达模式。我们使用共识聚类算法识别了两个 ARG 亚组。基于两个 ARG 亚型的差异表达基因(DEGs),我们计算了风险评分,即 ARG_score,并将 COAD 患者分为不同的风险组。为了研究 ARG_score 相关基因的表达,我们进行了 qRT-PCR。随后,我们将列线图映射出来,以便直观准确地描述 ARG_score 应用的价值。最后,我们探讨了 ARG_score 与临床特征、免疫浸润以及药物敏感性的相关性。

结果

我们鉴定了两个 ARG 相关亚组,它们在总生存期(OS)和肿瘤微环境方面存在显著差异。然后,我们基于 8 个 DEGs 构建了一个预测总生存期的 ARG_score,并在 COAD 患者中验证了其可靠的预测能力,较高的 ARG_score 与较差的预后相关。此外,我们通过 qRT-PCR 研究了 8 个 ARG_score 相关基因的表达。为了使 ARG_score 在临床上可行,我们创建了一个高度可靠的列线图。我们还发现,高危组中微卫星不稳定高(MSI-H)和肿瘤突变负荷(TMB)的比例较高。此外,ARG_score 与癌症干细胞指数和药物敏感性显著相关。

结论

该评分模型在 COAD 的预后、免疫微环境和治疗药物敏感性方面具有潜在的临床应用价值,为个性化治疗提供了新的思路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/d0cc0a425a86/fimmu-13-1049485-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/082e1bde3f60/fimmu-13-1049485-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/806e0d23bd91/fimmu-13-1049485-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/e0b7f48580cc/fimmu-13-1049485-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/d61d55ee7890/fimmu-13-1049485-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/3b134425809c/fimmu-13-1049485-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/b940041038e9/fimmu-13-1049485-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/4f6594372c93/fimmu-13-1049485-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/47d1a6762e4a/fimmu-13-1049485-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/d0cc0a425a86/fimmu-13-1049485-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/082e1bde3f60/fimmu-13-1049485-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/806e0d23bd91/fimmu-13-1049485-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/e0b7f48580cc/fimmu-13-1049485-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/d61d55ee7890/fimmu-13-1049485-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/3b134425809c/fimmu-13-1049485-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/b940041038e9/fimmu-13-1049485-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/4f6594372c93/fimmu-13-1049485-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/47d1a6762e4a/fimmu-13-1049485-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18ec/9731117/d0cc0a425a86/fimmu-13-1049485-g009.jpg

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